Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium

Multivariable Predictive Model of Postoperative Delirium in Cardiac Surgery Patients: Proteomic and Demographic Contributions

BACKGROUND

Delirium after cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. In this study, we aimed to identify protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery.

METHODS

SomaScan analysis of 1305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (preoperative baseline timepoint [PREOP]) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the Enzyme-Linked Lectin Assay (ELLA) multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology.

RESULTS

Of the 115 patients, 21 (18.3%) developed delirium after surgery. The SomaScan proteome screening evidenced differential expression of 115 and 85 proteins in delirious patients compared to nondelirious preoperatively and at POD2, respectively ( P < .05). Following biological and methodological criteria, 12 biomarker candidates (Tukey's fold change [|tFC|] >1.4, Benjamini-Hochberg [BH]- P < .01) were selected for ELLA multiplex validation. Statistical analyses of model fit resulted in the combination of age, sex, and 3 proteins (angiopoietin-2; C-C motif chemokine 5; and metalloproteinase inhibitor 1; area under the curve [AUC] = 0.829) as the best performing predictive model for delirium. Analyses of pathways showed that delirium-associated proteins are involved in inflammation, glial dysfunction, vascularization, and hemostasis.

CONCLUSIONS

Our results support the identification of patients at higher risk of developing delirium after cardiac surgery using a multivariable model that combines demographic and physiological features, also bringing light to the role of immune and vascular dysregulation as underlying mechanisms.

Accumulating the key proteomic signatures associated with delirium: Evidence from systematic review

Delirium is a severe neuropsychiatric illness that occurs frequently in intensive care and postoperative units which results in prolonged hospital stays and increases patient's mortality and morbidity rates. This review focused on accumulating the common key proteomic signatures significantly associated with delirium. We carried out a systematic literature review of studies on delirium proteomic biomarkers published between 1st January 2000 and 31st December 2023 from the following electronic bibliographic databases including PubMed, Scopus, and EBSCOhost (CINAHL, Medline). A total of 1746 studies were identified and reviewed, and 78 studies were included in our review. The PRISMA guidelines, the PEO framework, and JBI quality assessment method were followed in this review to maintain the inclusion and exclusion criteria and risk of bias assessment. Most of the included studies were of the cohort (68%) and case-control (23%) design. We have accumulated a total of 313 proteins or gene encoded proteins of which 189 were unique. Among the unique proteins, we focused on the top 13 most investigated proteins (IL-6, CRP, IL-8, S100B, IL-10, TNF-a, IL-1b, Cortisol, MCP-1, GFAP, IGF-1, IL-1ra, and NFL) that are significantly associated with delirium. Most of these are cytokines and inflammatory proteins indicating a strong interconnection with delirium. There was remarkable inconsistency among the studies in reporting the specific potential proteomic biomarker. No single proteomic biomarker can be solely used to diagnose and predict delirium. The current review provides a rationale for further molecular investigation of delirium-related proteomic biomarkers. Also, it's recommended to conduct further in-depth molecular research to decipher drug target biomolecules for potential prognostic, diagnostic, and therapeutic development against delirium.

Integrated Multi-Omics Analysis of Cerebrospinal Fluid in Postoperative Delirium

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case-no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85-1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Risk stratification and predictive modeling of postoperative delirium in chronic subdural hematoma

Background- Postoperative delirium is a common complication associated with the elderly, causing increased morbidity and prolonged hospital stay. However, its risk factors in chronic subdural hematoma patients have not been well studied. Methods- A total of 202 consecutive patients with chronic subdural hematoma at Peking University Third Hospital between January 2018 and January 2023 were enrolled. Various clinical indicators were analyzed to identify independent risk factors for postoperative delirium using univariate and multivariate regression analyses. Delirium risk prediction models were developed as a nomogram and a Markov chain. Results- Out of the 202 patients (age, 71 (IQR, 18); female-to-male ratio, 1:2.7) studied, 63 (31.2%) experienced postoperative delirium. Univariate analysis identified age (p < 0.001), gender (p = 0.014), restraint belt use (p < 0.001), electrolyte imbalance (p < 0.001), visual analog scale score (p < 0.001), hematoma thickness (p < 0.001), midline shift (p < 0.001), hematoma side (p = 0.013), hematoma location (p = 0.018), and urinal catheterization (p = 0.028) as significant factors. Multivariate regression analysis confirmed the significance of restraint belt use (B = 7.657, p < 0.001), electrolyte imbalance (B = -3.993, p = 0.001), visual analog scale score (B = 2.331, p = 0.016), and midline shift (B = 0.335, p = 0.007). Hematoma thickness and age had no significant impact. Conclusion- Increased midline shift and visual analog scale scores, alongside restraint belt use and electrolyte imbalance elevate delirium risk in chronic subdural hematoma surgery. Our prediction models may offer reference value in this context.